
# import all packages and set plots to be embedded inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import datetime as dt
import plotly.express as px

%matplotlib inline

# import warning and ignore future warnings
import warnings
warnings.simplefilter(action="ignore", category=FutureWarning)


# preliminary assessment function

def assess_data(file, encoding='utf-8'):
    
    # read file
    df = pd.read_csv(file, encoding=encoding)
    
    # check header
    print("The First Five Observations in DataFrame")
    display(df.head())
    print('\n')
    
    # check tail
    print("The Last Five Observations in DataFrame")
    display(df.tail())
    print('\n')
    
    # check shape of df
    print("The Shape of DataFrame")
    print(df.shape)
    print('\n')
    
    # check info of df
    print("Basic Information of DataFrame")
    print('\n')
    display(df.info())
    print('\n')
    
    # check number of unique values in df
    print("Number of Unique Values in DataFrame")
    print('\n')
    print(df.nunique())
    print('\n')
    
    # check number of missing values in df
    print("Number of Missing Values in DataFrame")
    print('\n')
    print(df.isnull().sum())
    print('\n')
    
    # check number duplicates in df
    print("Number of Duplicates in DataFrame")
    print("Number of duplicates: ", df.duplicated().sum())
    
    return df


# load data and access data
bike_df = assess_data('201902-fordgobike-tripdata.csv')

The First Five Observations in DataFrame


The Last Five Observations in DataFrame


The Shape of DataFrame
(183412, 16)


Basic Information of DataFrame


<class 'pandas.core.frame.DataFrame'>
RangeIndex: 183412 entries, 0 to 183411
Data columns (total 16 columns):
 #   Column                   Non-Null Count   Dtype  
---  ------                   --------------   -----  
 0   duration_sec             183412 non-null  int64  
 1   start_time               183412 non-null  object 
 2   end_time                 183412 non-null  object 
 3   start_station_id         183215 non-null  float64
 4   start_station_name       183215 non-null  object 
 5   start_station_latitude   183412 non-null  float64
 6   start_station_longitude  183412 non-null  float64
 7   end_station_id           183215 non-null  float64
 8   end_station_name         183215 non-null  object 
 9   end_station_latitude     183412 non-null  float64
 10  end_station_longitude    183412 non-null  float64
 11  bike_id                  183412 non-null  int64  
 12  user_type                183412 non-null  object 
 13  member_birth_year        175147 non-null  float64
 14  member_gender            175147 non-null  object 
 15  bike_share_for_all_trip  183412 non-null  object 
dtypes: float64(7), int64(2), object(7)
memory usage: 22.4+ MB

None


Number of Unique Values in DataFrame


duration_sec                 4752
start_time                 183401
end_time                   183397
start_station_id              329
start_station_name            329
start_station_latitude        334
start_station_longitude       335
end_station_id                329
end_station_name              329
end_station_latitude          335
end_station_longitude         335
bike_id                      4646
user_type                       2
member_birth_year              75
member_gender                   3
bike_share_for_all_trip         2
dtype: int64


Number of Missing Values in DataFrame


duration_sec                  0
start_time                    0
end_time                      0
start_station_id            197
start_station_name          197
start_station_latitude        0
start_station_longitude       0
end_station_id              197
end_station_name            197
end_station_latitude          0
end_station_longitude         0
bike_id                       0
user_type                     0
member_birth_year          8265
member_gender              8265
bike_share_for_all_trip       0
dtype: int64


Number of Duplicates in DataFrame
Number of duplicates:  0


# make a copy of dataset
bike_df_clean = bike_df.copy()
bike_df_clean


# change start_time and end_time to datetime data type
column1 = ['start_time', 'end_time']

for c in column1:
    bike_df_clean[c] = pd.to_datetime(bike_df_clean[c])


# change start_station_id, end_station_id and bike_id to string data type
column2 = ['start_station_id', 'end_station_id', 'bike_id']

for c in column2:
    bike_df_clean[c] = bike_df_clean[c].astype('str')


# change user_type, member_gender and bike_share_for_all_trip to category data type
column3 = ['user_type', 'member_gender', 'bike_share_for_all_trip']

for c in column3:
    bike_df_clean[c] = bike_df_clean[c].astype('category')


# change member_birth_year to str
bike_df_clean['member_birth_year'] = bike_df_clean['member_birth_year'].astype('str')


# confirm data types
bike_df_clean.dtypes

duration_sec                        int64
start_time                 datetime64[ns]
end_time                   datetime64[ns]
start_station_id                   object
start_station_name                 object
start_station_latitude            float64
start_station_longitude           float64
end_station_id                     object
end_station_name                   object
end_station_latitude              float64
end_station_longitude             float64
bike_id                            object
user_type                        category
member_birth_year                  object
member_gender                    category
bike_share_for_all_trip          category
dtype: object


# check the summary statistic of the dataset
bike_df_clean.describe()


# create duration_min from duration_sec
bike_df_clean['duration_min'] = round(bike_df_clean['duration_sec'] / 60, 2)

# check header
bike_df_clean.head(1)


# create start_hour, start_day, start_week, and start_month from start_time
bike_df_clean['start_hour'] = bike_df_clean['start_time'].dt.hour
bike_df_clean['start_day'] = bike_df_clean['start_time'].dt.day_name()
bike_df_clean['start_week'] = bike_df_clean['start_time'].dt.week
bike_df_clean['start_month'] = bike_df_clean['start_time'].dt.month_name()

# check header
bike_df_clean.head(1)


# create end_hour, end_day, end_week, and end_month from end_time
bike_df_clean['end_hour'] = bike_df_clean['end_time'].dt.hour
bike_df_clean['end_day'] = bike_df_clean['end_time'].dt.day_name()
bike_df_clean['end_week'] = bike_df_clean['end_time'].dt.week
bike_df_clean['end_month'] = bike_df_clean['end_time'].dt.month_name()

# check header
bike_df_clean.head(1)


# save cleaned dataframe
bike_df_clean.to_csv('fordgobike_clean_data', encoding='utf-8')


# check header
bike_df_clean.head()


# check basic info of dataframe
bike_df_clean.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 183412 entries, 0 to 183411
Data columns (total 25 columns):
 #   Column                   Non-Null Count   Dtype         
---  ------                   --------------   -----         
 0   duration_sec             183412 non-null  int64         
 1   start_time               183412 non-null  datetime64[ns]
 2   end_time                 183412 non-null  datetime64[ns]
 3   start_station_id         183412 non-null  object        
 4   start_station_name       183215 non-null  object        
 5   start_station_latitude   183412 non-null  float64       
 6   start_station_longitude  183412 non-null  float64       
 7   end_station_id           183412 non-null  object        
 8   end_station_name         183215 non-null  object        
 9   end_station_latitude     183412 non-null  float64       
 10  end_station_longitude    183412 non-null  float64       
 11  bike_id                  183412 non-null  object        
 12  user_type                183412 non-null  category      
 13  member_birth_year        183412 non-null  object        
 14  member_gender            175147 non-null  category      
 15  bike_share_for_all_trip  183412 non-null  category      
 16  duration_min             183412 non-null  float64       
 17  start_hour               183412 non-null  int64         
 18  start_day                183412 non-null  object        
 19  start_week               183412 non-null  int64         
 20  start_month              183412 non-null  object        
 21  end_hour                 183412 non-null  int64         
 22  end_day                  183412 non-null  object        
 23  end_week                 183412 non-null  int64         
 24  end_month                183412 non-null  object        
dtypes: category(3), datetime64[ns](2), float64(5), int64(5), object(10)
memory usage: 31.3+ MB


bike_df_clean.end_month.unique()

array(['March', 'February'], dtype=object)


# choose a base color
base_color = sns.color_palette()[0]

# count the frequency of each unique value
freq = bike_df_clean['member_gender'].value_counts()

# get indexes of the series
gen_order = freq.index

# plot bar chart in decreasing order of frequency
sns.countplot(data=bike_df_clean, 
              x='member_gender', 
              color=base_color, 
              order=gen_order)
plt.xlabel('Gender of Members')
plt.ylabel('Frequency')
plt.title('Proportion of Gender distribution of Members');


# choose a base color
base_color = sns.color_palette()[0]

# plot bar chart
sns.countplot(data=bike_df_clean, x='user_type', color=base_color) 
plt.xlabel('Bike User Type')
plt.ylabel('Frequency')
plt.title('Proportion of Bike User Types');


# choose a base color
base_color = sns.color_palette()[0]

# plot bar chart
sns.countplot(data=bike_df_clean, x='bike_share_for_all_trip', color=base_color) 
plt.ylabel('Frequency')
plt.title('Proportion of Bike Share for All Trip');


# choose a base color
base_color = sns.color_palette()[0]

# count the frequency of each unique value
freq = bike_df_clean['member_birth_year'].value_counts()[:20]

# get indexes of the series
order = freq.index

# plot bar chart in decreasing order of frequency
plt.figure(figsize=(12, 5))
sns.countplot(data=bike_df_clean, 
              x='member_birth_year', 
              color=base_color, 
              order=order)
plt.xticks(rotation=15)
plt.xlabel('Birth Year of Members', fontsize=14)
plt.ylabel('Frequency')
plt.title('Top 20 Birth Year with The Highest Frequency', fontsize=14);


# check unique values in the member_birth_year in sorted order
np.sort(bike_df_clean.member_birth_year.unique())

array(['1878.0', '1900.0', '1901.0', '1902.0', '1910.0', '1920.0',
       '1927.0', '1928.0', '1930.0', '1931.0', '1933.0', '1934.0',
       '1938.0', '1939.0', '1941.0', '1942.0', '1943.0', '1944.0',
       '1945.0', '1946.0', '1947.0', '1948.0', '1949.0', '1950.0',
       '1951.0', '1952.0', '1953.0', '1954.0', '1955.0', '1956.0',
       '1957.0', '1958.0', '1959.0', '1960.0', '1961.0', '1962.0',
       '1963.0', '1964.0', '1965.0', '1966.0', '1967.0', '1968.0',
       '1969.0', '1970.0', '1971.0', '1972.0', '1973.0', '1974.0',
       '1975.0', '1976.0', '1977.0', '1978.0', '1979.0', '1980.0',
       '1981.0', '1982.0', '1983.0', '1984.0', '1985.0', '1986.0',
       '1987.0', '1988.0', '1989.0', '1990.0', '1991.0', '1992.0',
       '1993.0', '1994.0', '1995.0', '1996.0', '1997.0', '1998.0',
       '1999.0', '2000.0', '2001.0', 'nan'], dtype=object)


# Create bins with step-size 5
binsize = 100
bins = np.arange(0, bike_df_clean['duration_sec'].max()+binsize, binsize)

# specify fig size
plt.figure(figsize=(10, 5))
plt.hist(data=bike_df_clean, x='duration_sec', bins=bins)
plt.xlim([0, 5000])
plt.xlabel('Duration in seconds');


# the distribution is right skewed with long tail, a log scale should help to elucidate the distribution further
log_binsize = 0.025
bins = 10 ** np.arange(2.4, np.log10(bike_df_clean['duration_sec'].max())+log_binsize, log_binsize)

plt.figure(figsize=(10, 6))
plt.hist(data =bike_df_clean, x ='duration_sec', bins=bins)
plt.xscale('log')
plt.xlabel('Duration in seconds')
plt.title('Distribution of Bike Duration in Seconds');


# choose a base color
base_color = sns.color_palette()[0]

# count the frequency of each unique value
freq = bike_df_clean['start_station_name'].value_counts()[:10]

# get indexes of the series
order = freq.index

# plot bar chart in decreasing order of frequency
plt.figure(figsize=(10, 4))
sns.countplot(data=bike_df_clean, 
              y='start_station_name', 
              color=base_color, 
              order=order)
plt.ylabel('Start Station Name', fontsize=14)
plt.xlabel('Frequency of Use', fontsize=14)
plt.title('Top Ten Start Station with The Highest Frequency', fontsize=16);


# choose a base color
base_color = sns.color_palette()[0]

# count the frequency of each unique value
freq = bike_df_clean['end_station_name'].value_counts()[:10]

# get indexes of the series
order = freq.index

# plot bar chart in decreasing order of frequency
plt.figure(figsize=(10, 4))
sns.countplot(data=bike_df_clean, 
              y='end_station_name', 
              color=base_color, 
              order=order)
plt.ylabel('End Station Name', fontsize=14)
plt.xlabel('Frequency of Use', fontsize=14)
plt.title('Top Ten End Station with The Highest Frequency', fontsize=16);


# set figsize
plt.figure(figsize = [20, 5])

# choose a base color
base_color = sns.color_palette()[0]

# set order of x-axis
order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']

# BARPLOT OF START DAY ON LEFT
plt.subplot(1, 2, 1)
sns.countplot(data=bike_df_clean, x='start_day', color=base_color, order=order)
plt.xlabel('Start Day of Ride', fontsize=14)
plt.title('Distribution of Start Day of Ride', fontsize=16);

# BARPLOT OF END DAY ON RIGHT 
plt.subplot(1, 2, 2)
sns.countplot(data=bike_df_clean, x='end_day', color=base_color, order=order)
plt.xlabel('End Day of Ride', fontsize=14)
plt.title('Distribution of End Day of Ride', fontsize=16);


# set figsize
plt.figure(figsize = [20, 5])

# choose a base color
base_color = sns.color_palette()[0]

# set order of x-axis
order = range(1, 24)

# BARPLOT OF START HOUR ON LEFT
plt.subplot(1, 2, 1)
sns.countplot(data=bike_df_clean, x='start_hour', color=base_color, order=order)
plt.xlabel('Start Hour of Ride', fontsize=14)
plt.title('Distribution of Start Hour of Ride', fontsize=16);

# BARPLOT OF END HOUR ON RIGHT 
plt.subplot(1, 2, 2)
sns.countplot(data=bike_df_clean, x='end_hour', color=base_color, order=order)
plt.xlabel('End Hour of Ride', fontsize=14)
plt.title('Distribution of End Hour of Ride', fontsize=16);


# check header
bike_df_clean.head()


# get the lat and lon value of "Market St at 10th St" station
bike_df_clean.query('start_station_name == "Market St at 10th St"').head(1)


fig = px.scatter_mapbox(
    bike_df_clean,
    lat="start_station_latitude",
    lon="start_station_longitude",
    width=1000,  
    height=600,
    color="start_station_id",
    center={"lat": 37.776619, "lon": -122.417385}, # center map on Market St at 10th St
    hover_data=["start_station_name"]
)

fig.update_layout(mapbox_style="open-street-map")

fig.show()


# create correlation variable
corr = bike_df_clean.corr() 

# set figsize
plt.figure(figsize = [10, 5])

# visualize correlation heatmap
sns.heatmap(data= corr, cmap='viridis_r', annot=True);


plt.figure(figsize=[12, 5])
sns.boxplot(data=bike_df_clean, x='duration_min', y='member_gender', color=base_color);


# set random seed for reproductivity
np.random.seed(70)

# make a dataset of 1000 samples
bike_df_clean_samp = bike_df_clean.sample(n=1000)

# create boxplot to view relationship
plt.figure(figsize=[12, 5])
sns.boxplot(data=bike_df_clean_samp, x='duration_min', y='member_gender', color=base_color)
plt.xlabel('Duration per minute', fontsize=14)
plt.ylabel('Gender of Members', fontsize=14)
plt.title('Relationship between Gender of Members and Ride Duration', fontsize=16);


plt.figure(figsize=[12, 5])
sns.violinplot(data=bike_df_clean, x='duration_min', y='user_type', color=base_color);


# set random seed for reproductivity
np.random.seed(70)

# make a dataset of 1000 samples
bike_df_clean_samp = bike_df_clean.sample(n=1000)

# create boxplot to view relationship
plt.figure(figsize=[12, 5])
sns.violinplot(data=bike_df_clean_samp, x='duration_min', y='user_type', color=base_color)
plt.xlabel('Duration per minute', fontsize=14)
plt.ylabel('User Type', fontsize=14)
plt.title('Relationship between User Type and Ride Duration', fontsize=16);


# choose a base color
base_color = sns.color_palette()[0]

plt.figure(figsize=[12, 5])
sns.violinplot(data=bike_df_clean, x='duration_min', y='bike_share_for_all_trip', color=base_color);


# set random seed for reproductivity
np.random.seed(70)

# make a dataset of 1000 samples
bike_df_clean_samp = bike_df_clean.sample(n=1000)

# create boxplot to view relationship
plt.figure(figsize=[12, 5])
sns.violinplot(data=bike_df_clean_samp, x='duration_min', y='bike_share_for_all_trip', color=base_color)
plt.xlabel('Duration per minute', fontsize=14)
plt.ylabel('Bike Share for All Trip', fontsize=14)
plt.title('Relationship between Bike Share for All Trip and Ride Duration', fontsize=16);


# plot bar chart
plt.figure(figsize=[10, 5])
sns.countplot(data=bike_df_clean, x='user_type', hue='bike_share_for_all_trip') 
plt.xlabel('Bike User Type')
plt.ylabel('Frequency')
plt.title('Relationship between Bike User Types and Bike Share for All Trip');


plt.figure(figsize=[12, 5])
sns.pointplot(
    data=bike_df_clean,
    x="member_gender", 
    y="duration_min",
    hue="user_type",
    ci=None,
    join=False
)
plt.xlabel('Gender of Members', fontsize=14)
plt.ylabel('Duration per minute', fontsize=14)
plt.title('Duration of Rides against Gender of Members and User Type', fontsize=16);


# set order of values
order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']

# set figure size
plt.figure(figsize=[12, 5])

sns.barplot(
    data=bike_df_clean,
    x="start_day", 
    y="duration_min",
    hue="user_type",
    ci=None,
    order=order
);
plt.xticks(rotation=15)
plt.xlabel('Start Day of Trip', fontsize=14)
plt.ylabel('Duration per minute', fontsize=14)
plt.title('Duration of Rides against Start Day and User Type', fontsize=16);


# set order of values
order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']

# set figure size
plt.figure(figsize=[12, 5])

sns.barplot(
    data=bike_df_clean,
    x="start_day", 
    y="duration_min",
    hue='bike_share_for_all_trip',
    ci=None,
    order=order
);
plt.xticks(rotation=15)
plt.xlabel('Start Day of Trip', fontsize=14)
plt.ylabel('Duration per minute', fontsize=14)
plt.title('Duration of Rides against Start Day and Bike Share for All Trip', fontsize=16);

	duration_sec	start_time	end_time	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bike_id	user_type	member_birth_year	member_gender	bike_share_for_all_trip
0	52185	2019-02-28 17:32:10.1450	2019-03-01 08:01:55.9750	21.0	Montgomery St BART Station (Market St at 2nd St)	37.789625	-122.400811	13.0	Commercial St at Montgomery St	37.794231	-122.402923	4902	Customer	1984.0	Male	No
1	42521	2019-02-28 18:53:21.7890	2019-03-01 06:42:03.0560	23.0	The Embarcadero at Steuart St	37.791464	-122.391034	81.0	Berry St at 4th St	37.775880	-122.393170	2535	Customer	NaN	NaN	No
2	61854	2019-02-28 12:13:13.2180	2019-03-01 05:24:08.1460	86.0	Market St at Dolores St	37.769305	-122.426826	3.0	Powell St BART Station (Market St at 4th St)	37.786375	-122.404904	5905	Customer	1972.0	Male	No
3	36490	2019-02-28 17:54:26.0100	2019-03-01 04:02:36.8420	375.0	Grove St at Masonic Ave	37.774836	-122.446546	70.0	Central Ave at Fell St	37.773311	-122.444293	6638	Subscriber	1989.0	Other	No
4	1585	2019-02-28 23:54:18.5490	2019-03-01 00:20:44.0740	7.0	Frank H Ogawa Plaza	37.804562	-122.271738	222.0	10th Ave at E 15th St	37.792714	-122.248780	4898	Subscriber	1974.0	Male	Yes

	duration_sec	start_time	end_time	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bike_id	user_type	member_birth_year	member_gender	bike_share_for_all_trip
183407	480	2019-02-01 00:04:49.7240	2019-02-01 00:12:50.0340	27.0	Beale St at Harrison St	37.788059	-122.391865	324.0	Union Square (Powell St at Post St)	37.788300	-122.408531	4832	Subscriber	1996.0	Male	No
183408	313	2019-02-01 00:05:34.7440	2019-02-01 00:10:48.5020	21.0	Montgomery St BART Station (Market St at 2nd St)	37.789625	-122.400811	66.0	3rd St at Townsend St	37.778742	-122.392741	4960	Subscriber	1984.0	Male	No
183409	141	2019-02-01 00:06:05.5490	2019-02-01 00:08:27.2200	278.0	The Alameda at Bush St	37.331932	-121.904888	277.0	Morrison Ave at Julian St	37.333658	-121.908586	3824	Subscriber	1990.0	Male	Yes
183410	139	2019-02-01 00:05:34.3600	2019-02-01 00:07:54.2870	220.0	San Pablo Ave at MLK Jr Way	37.811351	-122.273422	216.0	San Pablo Ave at 27th St	37.817827	-122.275698	5095	Subscriber	1988.0	Male	No
183411	271	2019-02-01 00:00:20.6360	2019-02-01 00:04:52.0580	24.0	Spear St at Folsom St	37.789677	-122.390428	37.0	2nd St at Folsom St	37.785000	-122.395936	1057	Subscriber	1989.0	Male	No

	duration_sec	start_time	end_time	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bike_id	user_type	member_birth_year	member_gender	bike_share_for_all_trip
0	52185	2019-02-28 17:32:10.1450	2019-03-01 08:01:55.9750	21.0	Montgomery St BART Station (Market St at 2nd St)	37.789625	-122.400811	13.0	Commercial St at Montgomery St	37.794231	-122.402923	4902	Customer	1984.0	Male	No
1	42521	2019-02-28 18:53:21.7890	2019-03-01 06:42:03.0560	23.0	The Embarcadero at Steuart St	37.791464	-122.391034	81.0	Berry St at 4th St	37.775880	-122.393170	2535	Customer	NaN	NaN	No
2	61854	2019-02-28 12:13:13.2180	2019-03-01 05:24:08.1460	86.0	Market St at Dolores St	37.769305	-122.426826	3.0	Powell St BART Station (Market St at 4th St)	37.786375	-122.404904	5905	Customer	1972.0	Male	No
3	36490	2019-02-28 17:54:26.0100	2019-03-01 04:02:36.8420	375.0	Grove St at Masonic Ave	37.774836	-122.446546	70.0	Central Ave at Fell St	37.773311	-122.444293	6638	Subscriber	1989.0	Other	No
4	1585	2019-02-28 23:54:18.5490	2019-03-01 00:20:44.0740	7.0	Frank H Ogawa Plaza	37.804562	-122.271738	222.0	10th Ave at E 15th St	37.792714	-122.248780	4898	Subscriber	1974.0	Male	Yes
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
183407	480	2019-02-01 00:04:49.7240	2019-02-01 00:12:50.0340	27.0	Beale St at Harrison St	37.788059	-122.391865	324.0	Union Square (Powell St at Post St)	37.788300	-122.408531	4832	Subscriber	1996.0	Male	No
183408	313	2019-02-01 00:05:34.7440	2019-02-01 00:10:48.5020	21.0	Montgomery St BART Station (Market St at 2nd St)	37.789625	-122.400811	66.0	3rd St at Townsend St	37.778742	-122.392741	4960	Subscriber	1984.0	Male	No
183409	141	2019-02-01 00:06:05.5490	2019-02-01 00:08:27.2200	278.0	The Alameda at Bush St	37.331932	-121.904888	277.0	Morrison Ave at Julian St	37.333658	-121.908586	3824	Subscriber	1990.0	Male	Yes
183410	139	2019-02-01 00:05:34.3600	2019-02-01 00:07:54.2870	220.0	San Pablo Ave at MLK Jr Way	37.811351	-122.273422	216.0	San Pablo Ave at 27th St	37.817827	-122.275698	5095	Subscriber	1988.0	Male	No
183411	271	2019-02-01 00:00:20.6360	2019-02-01 00:04:52.0580	24.0	Spear St at Folsom St	37.789677	-122.390428	37.0	2nd St at Folsom St	37.785000	-122.395936	1057	Subscriber	1989.0	Male	No

	duration_sec	start_station_latitude	start_station_longitude	end_station_latitude	end_station_longitude
count	183412.000000	183412.000000	183412.000000	183412.000000	183412.000000
mean	726.078435	37.771223	-122.352664	37.771427	-122.352250
std	1794.389780	0.099581	0.117097	0.099490	0.116673
min	61.000000	37.317298	-122.453704	37.317298	-122.453704
25%	325.000000	37.770083	-122.412408	37.770407	-122.411726
50%	514.000000	37.780760	-122.398285	37.781010	-122.398279
75%	796.000000	37.797280	-122.286533	37.797320	-122.288045
max	85444.000000	37.880222	-121.874119	37.880222	-121.874119

	duration_sec	start_time	end_time	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	...	bike_share_for_all_trip	duration_min	start_hour	start_day	start_week	start_month	end_hour	end_day	end_week	end_month
0	52185	2019-02-28 17:32:10.145	2019-03-01 08:01:55.975	21.0	Montgomery St BART Station (Market St at 2nd St)	37.789625	-122.400811	13.0	Commercial St at Montgomery St	37.794231	...	No	869.75	17	Thursday	9	February	8	Friday	9	March
1	42521	2019-02-28 18:53:21.789	2019-03-01 06:42:03.056	23.0	The Embarcadero at Steuart St	37.791464	-122.391034	81.0	Berry St at 4th St	37.775880	...	No	708.68	18	Thursday	9	February	6	Friday	9	March
2	61854	2019-02-28 12:13:13.218	2019-03-01 05:24:08.146	86.0	Market St at Dolores St	37.769305	-122.426826	3.0	Powell St BART Station (Market St at 4th St)	37.786375	...	No	1030.90	12	Thursday	9	February	5	Friday	9	March
3	36490	2019-02-28 17:54:26.010	2019-03-01 04:02:36.842	375.0	Grove St at Masonic Ave	37.774836	-122.446546	70.0	Central Ave at Fell St	37.773311	...	No	608.17	17	Thursday	9	February	4	Friday	9	March
4	1585	2019-02-28 23:54:18.549	2019-03-01 00:20:44.074	7.0	Frank H Ogawa Plaza	37.804562	-122.271738	222.0	10th Ave at E 15th St	37.792714	...	Yes	26.42	23	Thursday	9	February	0	Friday	9	March

Part I - (Ford GoBike Exploration Data Analysis)¶

by (Muhammed Balogun)¶

Introduction¶

Preliminary Wrangling¶

Summary of Preliminary Assessment¶

Data Cleaning¶

Missing Values¶

Data Types¶

Creating New Columns from the Existing Ones¶

What is the structure of your dataset?¶

What is/are the main feature(s) of interest in your dataset?¶

What features in the dataset do you think will help support your investigation into your feature(s) of interest?¶

Univariate Exploration¶

What is the proportion of member gender?¶

What is the distribution of bike user type?¶

Which birth year is the most predominant among bike riders?¶

What is the distribution of duration_sec column?¶

What are the top ten start and end station names with the highest rides?¶

Which day of the week is most engaged by bike riders?¶

Which hour of the day is the peak bike ride hour?¶

Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?¶

Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?¶

Bivariate Exploration¶

What is the geographical distribution of bike users?¶

What is the correlation of the numeric variables?¶

How does member gender affects the duration of ride?¶

What is the relationship between user_type and duration of ride?¶

Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?¶

Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?¶

Multivariate Exploration¶

What is the combine effect of member gender and user type on the duration of ride?¶

How does start day of ride and user type affect the duration of ride?¶

Conclusions¶

Part I - (Ford GoBike Exploration Data Analysis)¶

by (Muhammed Balogun)¶

Introduction¶

Preliminary Wrangling¶

Summary of Preliminary Assessment¶

Data Cleaning¶

Missing Values¶

Data Types¶

Creating New Columns from the Existing Ones¶

What is the structure of your dataset?¶

What is/are the main feature(s) of interest in your dataset?¶

What features in the dataset do you think will help support your investigation into your feature(s) of interest?¶

Univariate Exploration¶

What is the proportion of member gender?¶

What is the distribution of bike user type?¶

What is the distribution of bike share for all trip?¶

Which birth year is the most predominant among bike riders?¶

What is the distribution of duration_sec column?¶

What are the top ten start and end station names with the highest rides?¶

Which day of the week is most engaged by bike riders?¶

Which hour of the day is the peak bike ride hour?¶

Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?¶

Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?¶

Bivariate Exploration¶

What is the geographical distribution of bike users?¶

What is the correlation of the numeric variables?¶

How does member gender affects the duration of ride?¶

What is the relationship between user_type and duration of ride?¶

Does bike sharing infulence the duration of ride?¶

What is the relationship between user type and bike share for all trip?¶

Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?¶

Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?¶

Multivariate Exploration¶

What is the combine effect of member gender and user type on the duration of ride?¶

How does start day of ride and user type affect the duration of ride?¶

How bike sharing and start day affect the length of ride?¶

Conclusions¶